AI-driven transformation in forensic medicine education: applications, pedagogical shifts, and future challenges

TL;DR

This paper explores how AI can transform forensic medicine education by enhancing teaching methods, content delivery, and evaluations, while addressing current challenges and limitations.

Contribution

The paper introduces strategies for integrating AI into forensic medicine education, focusing on pedagogical innovation and practical implementation.

Findings

AI enables virtual simulation instruction and intelligent case analysis in forensic medicine education.

Personalized learning pathways can be developed through AI-driven systems.

Ethical and technical challenges remain barriers to widespread AI adoption in this field.

Abstract

Forensic medicine, as an interdisciplinary field featuring a high degree of practicality and technological integration, is currently faced with several challenges, including limited teaching resources, few practical training opportunities, and slow adoption of emerging technologies. With the rapid advancement of artificial intelligence (AI), its growing application in medical education has significantly transformed the landscape of traditional teaching approaches in forensic medicine, opening up new possibilities for innovations in pedagogical models. Against this backdrop, this study examines the potential of AI technology in forensic medicine from the perspectives of pedagogical model innovation, content delivery, and evaluation system reform, with specific emphasis on virtual simulation instruction, intelligent case analysis, and personalized learning pathways. Furthermore, it discusses existing ethical, technical, and resource-related limitations in current research. Based on this study, the paper proposes strategies and practical pathways to promote the application of AI in forensic medicine education, aiming to provide theoretical insights for cultivating high-quality forensic medicine professionals.

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